Toward Reliable and Efficient Message Passing Software for HPC Systems: Fault Tolerance and Vector Extension

As the scale of High-performance Computing (HPC) systems continues to grow, researchers are devoted themselves to achieve the best performance of running long computing jobs on these systems. My research focus on reliability and efficiency study for HPC software. First, as systems become larger, mean-time-to-failure (MTTF) of these HPC systems is negatively impacted and tends to decrease. Handling system failures becomes a prime challenge. My research aims to present a general design and implementation of an efficient runtime-level failure detection and propagation strategy targeting large-scale, dynamic systems that is able to detect both node and process failures. Using multiple overlapping topologies to optimize the detection and propagation, minimizing the incurred overhead sand guaranteeing the scalability of the entire framework. Results from different machines and benchmarks compared to related works shows that my design and implementation outperforms non-HPC solutions significantly, and is competitive with specialized HPC solutions that can manage only MPI applications. Second, I endeavor to implore instruction level parallelization to achieve optimal performance. Novel processors support long vector extensions, which enables researchers to exploit the potential peak performance of target architectures. Intel introduced Advanced Vector Extension (AVX512 and AVX2) instructions for x86 Instruction Set Architecture (ISA). Arm introduced Scalable Vector Extension (SVE) with a new set of A64 instructions. Both enable greater parallelisms. My research utilizes long vector reduction instructions to improve the performance of MPI reduction operations. Also, I use gather and scatter feature to speed up the packing and unpacking operation in MPI. The evaluation of the resulting software stack under different scenarios demonstrates that the approach is not only efficient but also generalizable to many vector architecture and efficient

Brain Tumour Classification Using Deep Features from Structural MRI

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Machine learning for the classification of atrial fibrillation utilizing seismo- and gyrocardiogram

A significant number of deaths worldwide are attributed to cardiovascular diseases (CVDs), accounting for approximately one-third of the total mortality in 2019, with an estimated 18 million deaths. The prevalence of CVDs has risen due to the increasing elderly population and improved life expectancy. Consequently, there is an escalating demand for higher-quality healthcare services. Technological advancements, particularly the use of wearable devices for remote patient monitoring, have significantly improved the diagnosis, treatment, and monitoring of CVDs. Atrial fibrillation (AFib), an arrhythmia associated with severe complications and potential fatality, necessitates prolonged monitoring of heart activity for accurate diagnosis and severity assessment. Remote heart monitoring, facilitated by ECG Holter monitors, has become a popular approach in many cardiology clinics. However, in the absence of an ECG Holter monitor, other remote and widely available technologies can prove valuable. The seismo- and gyrocardiogram signals (SCG and GCG) provide information about the mechanical function of the heart, enabling AFib monitoring within or outside clinical settings. SCG and GCG signals can be conveniently recorded using smartphones, which are affordable and ubiquitous in most countries. This doctoral thesis investigates the utilization of signal processing, feature engineering, and supervised machine learning techniques to classify AFib using short SCG and GCG measurements captured by smartphones. Multiple machine learning pipelines are examined, each designed to address specific objectives. The first objective (O1) involves evaluating the performance of supervised machine learning classifiers in detecting AFib using measurements conducted by physicians in a clinical setting. The second objective (O2) is similar to O1, but this time utilizing measurements taken by patients themselves. The third objective (03) explores the performance of machine learning classifiers in detecting acute decompensated heart failure (ADHF) using the same measurements as O1, which were primarily collected for AFib detection. Lastly, the fourth objective (O4) delves into the application of deep neural networks for automated feature learning and classification of AFib. These investigations have shown that AFib detection is achievable by capturing a joint SCG and GCG recording and applying machine learning methods, yielding satisfactory performance outcomes. The primary focus of the examined approaches encompassed (1) feature engineering coupled with supervised classification, and (2) iv automated end-to-end feature learning and classification using deep convolutionalrecurrent neural networks. The key finding from these studies is that SCG and GCG signals reliably capture the heart’s beating pattern, irrespective of the operator. This allows for the detection of irregular rhythm patterns, making this technology suitable for monitoring AFib episodes outside of hospital settings as a remote monitoring solution for individuals suspected to have AFib. This thesis demonstrates the potential of smartphone-based AFib detection using built-in inertial sensors. Notably, a short recording duration of 10 to 60 seconds yields clinically relevant results. However, it is important to recognize that the results for ADHF did not match the state-of-the-art achievements due to the limited availability of ADHF data combined with arrhythmias as well as the lack of a cardiopulmonary exercise test in the measurement setting. Finally, it is important to recognize that SCG and GCG are not intended to replace clinical ECG measurements or long-term ambulatory Holter ECG recordings. Instead, within the scope of our current understanding, they should be regarded as complementary and supplementary technologies for cardiovascular monitoring

Computer modeling and signal analysis of cardiovascular physiology

This dissertation aims to study cardiovascular physiology from the cellular level to the whole heart level to the body level using numerical approaches. A mathematical model was developed to describe electromechanical interaction in the heart. The model integrates cardio-electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced currents. A finite element based parallel simulation scheme was developed to investigate coupled electrical and mechanical functions. The developed model and numerical scheme were utilized to study cardiovascular dynamics at cellular, tissue and organ levels. The influence of ion channel blockade on cardiac alternans was investigated. It was found that the channel blocker may significantly change the critical pacing period corresponding to the onset of alternans as well as the alternans’ amplitude. The influence of electro-mechanical coupling on cardiac alternans was also investigated. The study supported the earlier assumptions that discordant alternans is induced by the interaction of conduction velocity and action potential duration restitution at high pacing rates. However, mechanical contraction may influence the spatial pattern and onset of discordant alternans. Computer algorithms were developed for analysis of human physiology. The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of various cardiac abnormalities. However, disturbances and mistakes may modify physiological waves in ECG and lead to wrong diagnoses. This dissertation developed advanced signal analysis techniques and computer software to detect and suppress artifacts and errors in ECG. These algorithms can help to improve the quality of health care when integrated into medical devices or services. Moreover, computer algorithms were developed to predict patient mortality in intensive care units using various physiological measures. Models and analysis techniques developed here may help to improve the quality of health care

Surgical skills modeling in cardiac ablation using deep learning

Cardiovascular diseases, a leading global cause of death, can be treated using Minimally Invasive Surgery (MIS) for various heart conditions. Cardiac ablation is an example of MIS, treating heart rhythm disorders like atrial fibrillation and the operation outcomes are highly dependent on the surgeon's skills. This procedure utilizes catheters, flexible endovascular devices inserted into the patient's blood vessels through a small incision. Traditionally, novice surgeons' performance is assessed in the Operating Room (OR) through surgical tasks. Unskilled behavior can lead to longer operations and inferior surgical outcomes. However, an alternative approach can be capturing surgeons' maneuvers and using them as input for an AI model to evaluate their skills outside the OR. To this end, two experimental setups were proposed to study the skills modelling for surgical behaviours. The first setup simulates the ablation procedure using a mechanical system with a synthetic heartbeat mechanism that measures contact forces between the catheter's tip and tissue. The second one simulates the cardiac catheterization procedure for the surgeon’s practice and records the user's maneuvers at the same time. The first task involved maintaining the force within a safe range while the tip of the catheter is touching the surface. The second task was passing a catheter’s tip through curves and level-intersection on a transparent blood vessel phantom. To evaluate attendees' demonstrations, it is crucial to extract maneuver models for both expert and novice surgeons. Data from participants, including novices and experts, performing the task using the experimental setups, is compiled. Deep recurrent neural networks are employed to extract the model of skills by solving a binary classification problem, distinguishing between expert and novice maneuvers. The results demonstrate the proposed networks' ability to accurately distinguish between novice and expert surgical skills, achieving an accuracy of over 92%

Adoption of Big Data and AI methods to manage medication administration and intensive care environments

Artificial Intelligence (AI) has proven to be very helpful in different areas, including the medical field. One important parameter for healthcare professionals’ decision-making process is blood pressure, specifically mean arterial pressure (MAP). The application of AI in medicine, more specifically in Intensive Care Units (ICU) has the potential to improve the efficiency of healthcare and boost telemedicine operations with access to real-time predictions from remote locations. Operations that once required the presence of a healthcare professional, can be done at a distance, which facing the recent COVID-19 pandemic, proved to be crucial. This dissertation presents a solution to develop an AI system capable of accurately predicting MAP values. Many ICU patients suffer from sepsis or septic shock, and they can be identified by the need for vasopressors, such as noradrenaline, to keep their MAP above 65 mm Hg. The presented solution facilitates early interventions, thereby minimising the risk to patients. The current study reviews various machine learning (ML) models, training them to predict MAP values. One of the challenges is to see how the different models behave during their training process and choose the most promising one to test in a controlled environment. The dataset used to train the models contains identical data to the one generated by bedside monitors, which ensures that the models’ predictions align with real-world scenarios. The medical data generated is processed by a separate component that performs data cleaning, after which is directed to the application responsible for loading, classifying the data and utilising the ML model. To increase trust between healthcare professionals and the system to be developed, it is also intended to provide insights into how the results are achieved. The solution was integrated, for validation, with one of the telemedicine hubs deployed by the European project ICU4Covid through its CPS4TIC component.A Inteligência Artificial (IA) é muito útil em diferentes áreas, incluindo a saúde. Um parâmetro importante para a tomada de decisão dos profissionais de saúde é a pressão arterial, especificamente a pressão arterial média (PAM). A aplicação da IA na medicina, mais especificamente nas Unidades de Cuidados Intensivos (UCI), tem o potencial de melhorar a eficiência dos cuidados de saúde e impulsionar operações de telemedicina com acesso a previsões em tempo real a partir de locais remotos. As operações que exigiam a presença de um profissional de saúde, podem ser feitas à distância, o que, face à recente pandemia da COVID-19, se revelou crucial. Esta dissertação apresenta como solução um sistema de IA capaz de prever valores de PAM. Muitos pacientes nas UCI sofrem de sepse ou choque séptico, e podem ser identificados pela necessidade de vasopressores, como a noradrenalina, para manter a sua PAM acima dos 65 mm Hg. A solução apresentada facilita intervenções antecipadas, minimizando o risco para doentes. O estudo atual analisa vários modelos de machine learning (ML), e treina-os para preverem valores de PAM. Um desafio é ver o desempenho dos diferentes modelos durante o seu treino, e escolher o mais promissor para testar num ambiente controlado. O dataset utilizado para treinar os modelos contém dados idênticos aos gerados por monitores de cabeceira, o que assegura que as previsões se alinhem com cenários realistas. Os dados médicos gerados são processados por um componente separado responsável pela sua limpeza e envio para a aplicação responsável pelo seu carregamento, classificação e utilização do modelo ML. Para aumentar a confiança entre os profissionais de saúde e o sistema, pretende-se também fornecer uma explicação relativa à previsão dada. A solução foi integrada, para validação, com um dos centros de telemedicina implantado pelo projeto europeu ICU4Covid através da sua componente CPS4TIC